Theory and application of deorientation for target scattering Part I: deorientation theory
Bibliographic record
Abstract
Deorienation theory of polarimetric scattering targets is presented. Using transformation of the target scattering vector, the target orientation is turned to a certain fixed state and polarimetric scattering of the transformed scattering vector shows the prominence of the generic characteristics of target. By applying deorientation analysis to the principal eigenvector of coherency matrix derived from each pixel of SAR imagery, deorientation can be extended and applied to PolSAR data processing. The first part derives the transformation formula of deorientation, and defines a new set of parameters u,ν,ω,Ψ whose physical meaning is analyzed by scattering simulation of single non-spherical particle. The second part will design an unsupervised classification scheme based on(u,ν,Ψ)-H. And as examples, a SIR-C polarimetric image over China's Guangdong Hui-Yang district and an AirSAR polarimetric image over Canada′s Boreal district are classified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".